Silk-based wearable devices for health monitoring and medical treatment.

Previous works have focused on enhancing the tensile properties, mechanical flexibility, biocompatibility, and biodegradability of wearable devices for real-time and continuous health management. Silk proteins, including silk fibroin (SF) and sericin, show great advantages in wearable devices due to their natural biodegradability, excellent biocompatibility, and low fabrication cost. Moreover, these silk proteins possess great potential for functionalization and are being explored as promising candidates for multifunctional wearable devices with sensory capabilities and therapeutic purposes. This review introduces current advancements in silk-based constituents used in the assembly of wearable sensors and adhesives for detecting essential physiological indicators, including metabolites in body fluids, body temperature, electrocardiogram (ECG), electromyogram (EMG), pulse, and respiration. SF and sericin play vital roles in addressing issues related to discomfort reduction, signal fidelity improvement, as well as facilitating medical treatment. These developments signify a transition from hospital-centered healthcare toward individual-centered health monitoring and on-demand therapeutic interventions.

Improved PAA algorithm for breast mass detection in mammograms.

Mammography screening is instrumental in the early detection and diagnosis of breast cancer by identifying masses in mammograms. With the rapid development of deep learning, numerous deep learning-based object detection algorithms have been explored for mass detection studies. However, these methods often yield a high false positive rate per image (FPPI) while achieving a high true positive rate (TPR). To maintain a higher TPR while also ensuring lower FPPI, we improved the Probability Anchor Assignment (PAA) algorithm to enhance the detection capability for mammographic characteristics with our previous work. We considered three dimensions: the backbone network, feature fusion module, and dense detection heads. The final experiment showed the effectiveness of the proposed method, and the TPR/FPPI values of the final improved PAA algorithm were 0.96/0.56 on the INbreast datasets. Compared to other methods, our method stands distinguished with its effectiveness in addressing the imbalance between positive and negative classes in cases of single lesion detection.

Breast cancer detection and classification in mammogram using a three-stage deep learning framework based on PAA algorithm.

In recent years, deep learning has been used to develop an automatic breast cancer detection and classification tool to assist doctors. In this paper, we proposed a three-stage deep learning framework based on an anchor-free object detection algorithm, named the Probabilistic Anchor Assignment (PAA) to improve diagnosis performance by automatically detecting breast lesions (i.e., mass and calcification) and further classifying mammograms into benign or malignant. Firstly, a single-stage PAA-based detector roundly finds suspicious breast lesions in mammogram. Secondly, we designed a two-branch ROI detector to further classify and regress these lesions that aim to reduce the number of false positives. Besides, in this stage, we introduced a threshold-adaptive post-processing algorithm with dense breast information. Finally, the benign or malignant lesions would be classified by an ROI classifier which combines local-ROI features and global-image features. In addition, considering the strong correlation between the task of detection head of PAA and the task of whole mammogram classification, we added an image classifier that utilizes the same global-image features to perform image classification. The image classifier and the ROI classifier jointly guide to enhance the feature extraction ability and further improve the performance of classification. We integrated three public datasets of mammograms (CBIS-DDSM, INbreast, MIAS) to train and test our model and compared our framework with recent state-of-the-art methods. The results show that our proposed method can improve the diagnostic efficiency of radiologists by automatically detecting and classifying breast lesions and classifying benign and malignant mammograms.

Oxidative stress in vascular calcification.

Vascular calcification (VC), which is closely associated with significant mortality in cardiovascular disease, chronic kidney disease (CKD), and/or diabetes mellitus, is characterized by abnormal deposits of hydroxyapatite minerals in the arterial wall. The impact of oxidative stress (OS) on the onset and progression of VC has not been well described. Nicotinamide adenine dinucleotide phosphate (NADPH) oxidases, xanthine oxidases, myeloperoxidase (MPO), nitric oxide synthases (NOSs), superoxide dismutase (SOD) and paraoxonases (PONs) are relevant factors that influence the production of reactive oxygen species (ROS). Furthermore, excess ROS-induced OS has emerged as a critical mediator promoting VC through several mechanisms, including phosphate balance, differentiation of vascular smooth muscle cells (VSMCs), inflammation, DNA damage, and extracellular matrix remodeling. Because OS is a significant regulator of VC, antioxidants may be considered as novel treatment options.

Automated mammographic mass detection using deformable convolution and multiscale features.

Designing computer-assisted diagnosis (CAD) systems that can precisely identify lesions from mammography images would be useful for clinicians. Considering the morphological variation in breast cancer, it is necessary to extract robust features from the mammogram. Here, we propose a mass detection CAD system that is based on Faster R-CNN. First, we applied a novel convolution network in the backbone of Faster R-CNN, namely deformable convolution network (DCN), which improves the detection of lesions with varying shapes and sizes. Second, the original Faster R-CNN uses the output of the last layer of the backbone as a single-scale feature map. To facilitate the detection of small lesions, we used a multiscale feature pyramid network of multiple cross-scale connections between the different output layers of the backbone, called the neural architecture search-feature pyramid network (NAS-FPN). Thus, we were able to integrate the best features into the model. We then evaluated our method by using the datasets the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, respectively. Our method yielded a true positive rate of 0.9345 at 2.2805 false positive per image on CBIS-DDSM and a true positive rate of 0.9554 at 0.3829 false positive per image on INbreast. Graphical abstract.

SUMOylation in atherosclerosis.

Atherosclerosis (AS) is the pathophysiologic basis of many cardiovascular diseases. A number of studies have shown that post-translational modification (PTM) contributes to the initiation and progression of AS. For example, recent studies found that SUMOylation, ie, small ubiquitin-like modifier (SUMO) conjugation to target substrate proteins, was involved in AS. This PTM appears related to endothelial cell dysfunction (ECD), dyslipidemia and vascular smooth muscle cell (VSMC) proliferation. This review focuses on the molecular effects of SUMOylation in the initiation and progression of AS, including ECD, dyslipidemia and VSMC proliferation to better understand this pathologic process.

Scinderin-knockdown inhibits proliferation and promotes apoptosis in human breast carcinoma cells.

Previous studies have reported that scinderin (SCIN) affects multiple cellular processes, including proliferation, migration and differentiation in cancer. However, the specific role of SCIN in breast cancer (BC) cells is unknown. Immunohistochemistry was used to investigate SCIN expression in 46 BC and 21 mammary fibroadenoma or fibroadenomatoid hyperplasia tissue samples. SCIN expression was ablated in MDA-MB-231 and T-47D cells using lentivirus-mediated small interfering RNA technology. Cell proliferation was tested using Celigo and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assays. Cell apoptosis was analyzed by measuring Caspase 3/7 activity and annexin-V staining. The results of the present study demonstrated that SCIN expression was elevated in BC tissues compared with mammary fibroadenoma or fibroadenomatoid hyperplasia tissues. Specifically, higher SCIN expression was observed in Ki-67-positive BC tissues (78.6%) compared with Ki-67-negative BC tissues. Furthermore, knockdown of SCIN expression in the BC cell lines significantly suppressed cell proliferation and induced apoptosis. The data presented in the present study indicate that SCIN serves an important role in the development of breast cancer.